Voice user interfaces are filled to the brim with human language. Human languages are beautiful; they are geographically diverse and filled to the brim with uncertainties. Some languages have words that derive their semantic meaning from their context; for others, meaning derives from the tone in which the speaker says it or the syntactical placement of the word in the sentence. In linguistics, there are five main categories to human language that help linguists understand how each language harmonizes into a unified whole: syntax, semantics, pragmatics, morphology and phonology. Because of how ambiguous human languages are and how uniquely innate they are, it may be evident that they would not translate smoothly into a computer. While partly true, it is an inevitably complex subject. Computational linguistics is the academic study of computing natural language. On the theoretical and artificially intelligent level, this is referred to as machine language learning. All of the above is used in voice user interfaces.

Machine language learning is a component of deep learning. Deep learning is a core part of artificial intelligence and is critical to an artificially intelligent system’s performance – whether text-based, voice-based, or neither. Machine language learning possesses many layers, with its main one being natural language processing – also commonly referred to as NLP. Like deep learning at the core of artificial intelligence, natural language processing is at the core of machine language learning. It is an essential component of the fundamentals of any computational linguistic algorithm. Natural language processing often goes together with natural language understanding (NLU), and together, they make up the most successful chatbots and other text and voice-based artificial intelligence systems.

The use of computation to store text-based data is not a new concept. Any simple computer programming language will have a text-based database. It thus makes sense to want to apply language analysis, language translation, conversational design, and language databasing to computation. From a computational perspective, computational linguists can draw from their interdisciplinary background and apply it to artificial intelligence in the form of conversational artificial intelligence within chatbots, virtual assistants, and even virtual reality.

Voice User Interfaces

Voice user interfaces such as those seen from Google (Google Home) or those as technologically iconic as Amazon’s Alexa have become commonplace within many people’s day-to-day lives. For example, FitBit Sense now allows one to talk to Amazon’s Alexa right from the watch itself with the press of a button. As visual interfaces begin to intertwine themselves with voice user interfaces, it is critical that we not only understand why they work but how this will impact our lives as humans who already use language to connect on many levels with other people. How this phenomenon will translate into chatbots is an entirely different narrative.

person reaching out to a robot
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As mentioned previously, machine language learning is at the heart of computational linguistics. Within artificial intelligence, cognitive scientists aim to create artificial intelligence that is ‘strong’ – ‘strong’ in that it does not rely on a user for its input. ‘Strong’ in that it can use past inputs and outputs to create new algorithms. Essentially, computational linguists and cognitive scientists want artificially intelligent systems and voice user interfaces that can learn and speak without the help of a human. They want a piece of artificial intelligence that can evolve with time, independent of any individual behind the scenes working on simulating the learning experience of the chatbot’s experiences with the user. We want the artificial intelligence system to be phenomenally conscious, not just simulate it.

Strong artificial intelligence systems such as those mentioned previously are indeed the future. With self-driving cars on the way and mobile GPS apps with voice user interfaces that are almost entirely voice-based, the push for complex voice user interfaces and conversational artificial intelligence will continue to grow. With the success of social media and social communication apps, it only makes sense that cognitive scientists and computational linguists would want to have chatbots and voice user interfaces that mirror human-to-human communication and interaction. Humans are social creatures, and we crave social interaction. Whether or not an artificially intelligent robot can satisfy that craving is up to our ability to make conversational AI and voice user interface systems naturalized.

READ MORE: Machine Learning 101: The Revolutionary Side of Artificial Intelligence

Unfortunately, an area with such high potential results in high expectations. Much like the expectation of strong artificial intelligence to understand us and evolve and learn from their experiences with the user, this would ultimately translate into a piece of conversational artificial intelligence or voice user interface that is more human-like than its predecessor. The study of cognitive science is, after all, to naturalize and formalize the human mind into a machine.

Why Personalization Requires Strong Conversational Artificial Intelligence

With the expectations of users and those creating the robot to be more human-like and therefore more personalized, it becomes apparent that conversational artificial intelligence branches out into a much more complex area of technology and technological development. To make voice user interfaces that are as engaging and realistic as a human at the other end of a telephone would be, we would need it to function as a human. It would need to be personalized to each and every user, not your stereotypical cookie-cutter chatbot. A significant problem with the rise of conversational AI is its lack of personalization and user-friendliness. At this point in time, it is still too robotic, and therefore, unfriendly.

Presently, chatbots and other voice user interfaces are believed by the average user to not handle complex inquiries and tasks. Synthesizing language and natural language processing to allow artificial intelligence systems to process and understand natural language is one thing. Providing personalized implementations of conversational AI to the user that tailors to them and their experiences in a human-like manner is an entirely different story – one that must technologically evolve as the digital era continues.

While hard to imagine, this is crucial to keep in mind whilst thinking about the future of artificial intelligence, chatbots, virtual assistants, mobile apps, and other viable pieces of technology and their future in marketing. Going beyond artificial intelligence that manually simulates learning from a human and actually learns from the user, their prompts and experiences are the next steps in creating artificial intelligence eco-systems that would have consumers be open to the idea that chatbots can, indeed, handle complex tasks in an empathetic manner. Communication automation is the goal, yes, but we must consider that having communication automation be personalized to the individual is the ultimate goal regarding a voice user interface’s strength as an artificially intelligent system.

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This is not to say that we have not already reached artificial intelligence that is strong and, thus, formalized and personalized. We are far from completing such a task with all the natural human languages that exist – especially for voice user interfaces. Unfortunately for machines, human language is not as innate to machines as they are to humans.

The Applications of Conversational AI

Fortunately, natural language processing and conversational artificial intelligence as a scientific discipline of computer science have uses that go beyond user experiences on mobile apps or other hyper-personalized experiences with technology. Because machine intelligence weaves itself into computational linguistics and all its subfields, it is important to note its uses beyond futuristic speech-based interaction with humans. 

Statistical natural language processing is a massive component of this aspect of conversational artificial intelligence. It involves complex computer algorithms combined with machine language learning, deep learning, and neural networks to analyze and categorize voice and/or text data. There are many aspects of technology used in – presumably, types of technology you are familiar with and have encountered. It is an important aspect of voice user interfaces, specifically.

For example, many websites want to check that you are not a robot. What is so ironic about this is that it is a robot making sure you are not a robot. They do this by asking you questions that are typically beyond most standardly automated robot capabilities. To put it more simply, they ask you questions that are more ‘human’ – questions that often require critical thinking, common sense, and a lack of repetition, all of which are core components to the ‘humanity’ of humans. Said subjects are also very complex and have yet to be fully formalized within pieces of artificial intelligence machinery.

Therefore, it is easy to see why conversational artificial intelligence combines with natural language processing and understanding to form a subcategory of artificial intelligence that does not revolve around a user. Instead, it relies upon its own unique machine language learning algorithms to help with spam detection (especially within the financial sector), thorough machine translation, social media rule-analysis and even summarizations of voices or texts.

Machine learning and artificial intelligence systems are becoming increasingly important in our day-to-day lives. We have become accustomed to asking our smart-home system to turn the light on, ordering delivery food with the tips of our fingers, and having our fit watch tell us how many steps we have taken that day. With technology advancing faster than ever, it is crucial that we are able to differentiate between the various types of artificial intelligence, as well as how each one builds upon one another.

Artificial intelligence as a concept is a science – just like computer science, neuroscience, or mathematics. What is interesting about artificial intelligence, though, is that it encompasses all three of said sciences and more. These sciences are often referred to as the cognitive sciences, as they all focus on the human brain and mind in relation to its implementation into technology. Its implementation into technology requires a fundamental understanding of machine learning, deep learning, and neural networking.

Those seeking a foundational understanding will be pleased to find out that there are three main types and categories of artificial intelligence that vary in their goals as systems. Similarly, there are three subcategories to artificial intelligence that help us delve deeper into what being artificially intelligent truly means in the context of intelligent beings. Thus, artificial intelligence is the external and all-encompassing category, whereas machine learning is a subcategory. Machine learning, alongside artificial neural networking and deep representational learning, is at the core of artificial intelligence and is crucial to its success. Machine learning, however, is philosophically complex to a much greater extent than basic artificial intelligence.

The Three Categories of Artificial Intelligence

Three Main Categories
Artificial Intelligence’s Three Main Categories

The three main categories or types of artificial intelligence are often referred to as artificial intelligence’s three different ‘faces’ – two of which you might have heard famously uttered by one of the founders of computer science, Alan Turing: “strong” and “weak” artificial intelligence. Weak artificial intelligence is also commonly referred to as GOFAI (Good Old Fashioned Artificial Intelligence), with algorithms that simulate phenomenal consciousness and thought without actually possessing phenomenal consciousness and thought.

Weak Artificial Intelligence

Weak artificial intelligence exists today, and has existed for many decades now. It is the kind of artificially intelligent system that has become woven into the fabric of our everyday lives. Weak artificial intelligence tends to algorithmically focus on solving a specific problem within a specific domain, such as what moves to play to win a game of Chess.

Weak artificial intelligence is weak in that it is limited in its capabilities; it can only perform one task or function at a time. Furthermore, weak artificial intelligence often relies on a human to set its parameters. This means that weak artificial intelligence has no learning algorithms beyond what the human has coded it to do. Essentially, weak artificial intelligence is simulating phenomenal consciousness without actually being phenomenally conscious. The human behind any given weak artificially intelligent system may be making one believe it is phenomenally conscious based on its actions or output. All it is doing in actuality is relaying information to the necessary processes, and repeatedly completing the algorithms coded into it. You have most likely interacted with a piece of weak artificial intelligence, whether it be an online chatbot or virtual talking assistant (such as Apple’s Siri).

Strong Artificial Intelligence

The second category is strong artificial intelligence. Strong artificial intelligence aims to replicate the intelligence of a human-being, alongside the multi-tasking ability of a human; in that it can understand, learn, and perform a variety of problems and solutions that builds upon and strengthens itself over time. The gradual self-evolvement of strong artificially intelligent systems based on experiences means that it is teaching itself new things based on experience, much like a human would! Some argue that strong artificial intelligence has yet to even exist. While this may hold some truth, it is important to note that complex algorithms such as predictive algorithms on human behaviour (such as those seen on Amazon that are used to calculate what you may like your next purchase to be) are a sign that strong artificial intelligence is on its way or already here.


The third remaining category is superintelligence, a form of even stronger-than-strong artificial intelligence that we have yet to create as humans. To artificial intelligence experts, this is the final form of artificial intelligence that would essentially replicate us humans and our mind’s capabilities wholly and identically. Some argue superintelligence would surpass human intelligence. It is the robot utopia (or dystopia) you have always heard and read about, or perhaps even imagined yourself. Artificial superintelligence would combine all subcategories of artificial intelligence, including machine-learning algorithms, neural networks, machine reasoning, and robotics – all into one giant artificially intelligent super-system that would not only match humans in their reasoning and consciousness, but surpass them. Cognitive science aims to naturalize and formalize the human mind into a machine, and this has yet to be done. Thus, superintelligence has yet to exist in our present, modern-day world.

Machine Learning
Could a machine learn by itself?

The Subcategories of Machine Learning

With the basics and potentials of artificially intelligent systems covered, let us think back to the basics of strong versus weak artificial intelligence. It was briefly touched upon that in weak artificial intelligence, humans often set the parameters and they do not learn beyond whatever the human codes them to do. Machine learning is therefore a subset of artificial intelligence, and a smaller one at that, with relations directly to strong artificially intelligent systems. Machine learning exclusively focuses on teaching computers how to learn and evolve, without the help of a human essentially doing the learning for them and having it coded in. Machine learning itself is a powerful tool and used by many industries.

READ MORE: What Is Machine Learning Used For?

Machine learning as a subset is typically broken down into three of its own subcategories: algorithms, datasets, and features.


Algorithms are at the core of machine learning, because the algorithm does not have to be done a particular way. Algorithms are unique in that each algorithm will solve a problem or produce an output with varying accuracy and speeds. In order to use algorithms to help computers learn, modify, and grow, we must combine many different algorithms that, when completed, would produce another algorithm all by itself that is new and different. An even deeper subset of machine learning is deep learning that exclusively focuses on algorithms similar to the human brain and our thought structure. What is so complex about deep learning algorithms in machine learning, however, is that even the most basic algorithms influenced by the structure of the human mind, such as common sense, are substantially more complex than they first appear to be. Naturalizing, replicating, and formalizing such an algorithm would take a lot of philosophical and neuro-scientific discussion and thought.


If one is familiar with computer science theory or even the fundamental basics of coding, then one will know that datasets are an important part of any machine and its ability to not only produce results, but to evolve as they should in machine learning. Datasets can take the form of dictionaries, lists, sets, etc. Each dataset contains important information, often vital to the completion, performance, and efficiency of algorithms. Datasets can include anything and everything from numbers, to pictures, to text.


In machine learning, features are the keys that unlock the door to the solution. They are vital pieces of data that tell the machine what to focus on. Features in machine learning often take the form of mathematical formulas that can be used to compare solutions to other solutions. This not only results in the most efficient solution, but a solution that can be analyzed as an experience to learn from when a new and comparable problem arises. This is similar to what a phenomenally conscious human would do; take the problems and solutions from a particular instance and subconsciously build upon it when a similar, correlated problem occurs. This is one step closer to strong artificial intelligence and a core component of it!

Machine learning is a vital component of artificial intelligence. They are, however, not to be confused with one another. Artificial intelligence is a massive category that encompasses every aspect of artificial intelligence science and cognitive science. This spans out into a category that is innumerably large and intricate. Machine-language learning, while just as complex as artificial intelligence, does not encompass the whole that artificial intelligence does. Machine learning is simply a subcategory of artificial intelligence that defines its inner workings. Machine learning is also not “better” than artificial intelligence, as it is simply a part of it.

We would not have certain kinds of powerful artificial intelligence without machine learning, such as self-driving cars or speech/pattern recognition. These are types of artificial intelligence that rely on machine learning to sustain itself by using machine learning’s classic formula: input, feature extraction, classification, and output. Machine learning and machine language learning in conversational artificial intelligence, alongside deep learning, are at the core of artificial intelligence. They are essential.

The eventual deepening of our understanding in machine learning will hopefully lead us to a time and place where artificial intelligence and cognitive scientists are comfortable with machines’ potential and ability to outperform humans. In some cases, machines already are outperforming humans. This is because computing simple algorithms are far faster and more reliable than a human brain computing the same simple algorithm. This ultimately begs the question: is the human brain just a machine? We have yet to find out.

Machine learning is a branch of artificial intelligence (or AI), where computer algorithms are programmed to improve automatically as they continue to be used. Essentially, machine learning is the process of computers following a set of rules for collecting and analyzing and then solving problems or making decisions based on that same data. Related to statistics and data collection, it’s sometimes called predictive analytics. The algorithms help machines “learn” by giving the computer a specific task to complete, typically involving calculations or data collection, analysis and insight creation.

3 Different Types of Machine Learning 

Machine learning comes in three different types: 

  • Supervised
  • Unsupervised
  • Reinforcement learning 

Technology Review used the analysis of dog training to explain these types of machine learning in a much less tech-lingo-intensive way. It’s a good metaphor, so we’re using it too. 

Supervised learning is when a dog is trained to sniff out a specific scent. Truffle hunting dogs that search through forested areas to help handlers find truffles are an example. Essentially, the computer is looking for a specific pattern that the programmer has told it to search for in the algorithm it’s following. 

An example of unsupervised learning, in contrast, would be a dog trained to sniff out a collection of things, and not just one specific scent. A food sniffing dog in an airport could be one, as well as a dog that can pick out all the balls in a bin full of assorted toys. The computer combs through data, looking for any and all patterns, and grouping them together. This is often associated with data mining, where the algorithm is interested in identifying a particular class of information or trends.

Reinforcement learning is similar to the process of training the dog. You use treats to reward correct behaviour and withhold treats for incorrect behaviour. This is how facial recognition software on social media works. Facebook has access to many photos of people. It analyzes data and based on past photos, guesses which of your friends might be in the photo. You confirm either yes or no, and the system learns a little more. The more data is collected over time, the more accurate it gets in predicting who is in the photo. You are essentially training the machine, while also carefully collating and tagging your online photos.

So, What Is Machine Learning Used For?

Machine learning is being used in many different industries around the world. Examples that you encounter every day include search engines, email spam filters, fraud detection, music streaming apps, online digital assistants, and much more. Industry-specific examples are described in more detail below, but companies can use machine learning technology across departments to: 

  • Scale their customer service and meet people where they are 24/7 
  • Enhance the customer experience by providing faster and more accurate help
  • Become more efficient in their operations by identifying where time is lost or where customers are not interested
  • Predict future trends based on historical data in order to guide businesses based on facts, not simply human vision and prediction

Fraud Detection In Finance

The financial sector uses machine learning to improve customer safety and protect their customers’ finances. Machine learning can identify fraudulent transactions as soon as they happen, based on historical data. The machine can immediately identify and shut down or disallow the fraudulent transactions, freezing access, and notifying the user within minutes. Machine learning is helping the finance industry better protect their clients’ money and their own investments. 

netflix on an imac - machine learning
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Product Recommendations

Ever wonder why NETFLIX is recommending cartoons instead of your preferred home improvement shows? If your kid gets to spend more time watching their shows than you do, NETFLIX recommends more of that. Product recommendation machine learning isn’t just limited to shows. It’s used on many different websites for online shopping, advertising, YouTube videos, and Spotify.

Sales and Marketing Efficiencies

Companies are also using machine learning to help deliver a better customer experience. A CRM (customer relationship management) system can categorize your customers, and put them into specific marketing or sales streams based on their personal interests. It can also score leads in your system, analyzing users based on interactions with your content, to help you determine which customers are most likely to buy your product. Your sales teams will be able to spend more time engaging with leads that are already warmed-up, rather than needing to partake in cold calls.

CRMs use machine learning to give you a more complete understanding of your customers, so you can give them a better customer experience. Machine learning can identify issues that are arising and flag the pattern so you can solve problems before they become larger. You can also tap into the data around what your customers enjoy, how frequently they like to be emailed, and what services they find especially useful.

Chatbots Use Machine Learning to Improve Customer Experience

One way to begin collecting data on your customers’ needs and preferences is through the use of a chatbot. It’s also a perfect, low-risk place to start with machine learning. Because you can train the chatbot on specific sets of data and release it to a small audience to start, you control the roll-out and can improve its performance in real-time with the development team. 

You also minimize risk with a chatbot because you can carefully design it to solve just one problem. Our philosophy is to begin with one use case, proven to impact the business positively. The minimum viable product (or MVP) is then designed to meet that one need, without impacting any of your other business systems. You can begin experimenting with the powerful data collection and machine learning capabilities, at your own pace, without needing to upend any of your current processes or tech stacks. As you get more comfortable with the technology, chatbots can be integrated with your existing stacks to ensure a seamless integration.
Get in touch with one of our consultants today to learn more about how to begin putting machine learning to work. We’re experts in assigning the right machine learning technology to your specific use case in order to deliver an innovative solution to you faster.